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resnet.py
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# Copyright (c) 2021 Shuai Wang ([email protected])
# 2022 Zhengyang Chen ([email protected])
# 2023 Bing Han ([email protected])
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
'''ResNet in PyTorch.
Some modifications from the original architecture:
1. Smaller kernel size for the input layer
2. Smaller number of Channels
3. No max_pooling involved
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import pooling_layers as pooling_layers
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes,
self.expansion * planes,
kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(self.expansion * planes))
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self,
block,
num_blocks,
m_channels=32,
feat_dim=40,
embed_dim=128,
pooling_func='TSTP',
two_emb_layer=True):
super(ResNet, self).__init__()
self.in_planes = m_channels
self.feat_dim = feat_dim
self.embed_dim = embed_dim
self.stats_dim = int(feat_dim / 8) * m_channels * 8
self.two_emb_layer = two_emb_layer
self.conv1 = nn.Conv2d(1,
m_channels,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(m_channels)
self.layer1 = self._make_layer(block,
m_channels,
num_blocks[0],
stride=1)
self.layer2 = self._make_layer(block,
m_channels * 2,
num_blocks[1],
stride=2)
self.layer3 = self._make_layer(block,
m_channels * 4,
num_blocks[2],
stride=2)
self.layer4 = self._make_layer(block,
m_channels * 8,
num_blocks[3],
stride=2)
self.pool = getattr(pooling_layers,
pooling_func)(in_dim=self.stats_dim *
block.expansion)
self.pool_out_dim = self.pool.get_out_dim()
self.seg_1 = nn.Linear(self.pool_out_dim, embed_dim)
if self.two_emb_layer:
self.seg_bn_1 = nn.BatchNorm1d(embed_dim, affine=False)
self.seg_2 = nn.Linear(embed_dim, embed_dim)
else:
self.seg_bn_1 = nn.Identity()
self.seg_2 = nn.Identity()
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = x.permute(0, 2, 1) # (B,T,F) => (B,F,T)
x = x.unsqueeze_(1)
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
stats = self.pool(out)
embed_a = self.seg_1(stats)
if self.two_emb_layer:
out = F.relu(embed_a)
out = self.seg_bn_1(out)
embed_b = self.seg_2(out)
return embed_a, embed_b
else:
return torch.tensor(0.0), embed_a
def ResNet18(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(BasicBlock, [2, 2, 2, 2],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet34(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(BasicBlock, [3, 4, 6, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet50(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(Bottleneck, [3, 4, 6, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet101(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(Bottleneck, [3, 4, 23, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet152(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(Bottleneck, [3, 8, 36, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet221(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(Bottleneck, [6, 16, 48, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
def ResNet293(feat_dim, embed_dim, pooling_func='TSTP', two_emb_layer=True):
return ResNet(Bottleneck, [10, 20, 64, 3],
feat_dim=feat_dim,
embed_dim=embed_dim,
pooling_func=pooling_func,
two_emb_layer=two_emb_layer)
if __name__ == '__main__':
x = torch.zeros(10, 200, 80)
model = ResNet34(feat_dim=80, embed_dim=256, pooling_func='MQMHASTP')
model.eval()
out = model(x)
print(out[-1].size())
num_params = sum(p.numel() for p in model.parameters())
print("{} M".format(num_params / 1e6))
# from thop import profile
# x_np = torch.randn(1, 200, 80)
# flops, params = profile(model, inputs=(x_np, ))
# print("FLOPS: {} G, Params: {} M".format(flops / 1e9, params / 1e6))